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Node fingerprinting: an efficient heuristic for aligning biological networks

journal contribution
posted on 2023-05-18, 09:39 authored by Radu, A, Michael CharlestonMichael Charleston
With the continuing increase in availability of biological data and improvements to biological models, biological network analysis has become a promising area of research. An emerging technique for the analysis of biological networks is through network alignment. Network alignment has been used to calculate genetic distance, similarities between regulatory structures, and the effect of external forces on gene expression, and to depict conditional activity of expression modules in cancer. Network alignment is algorithmically complex, and therefore we must rely on heuristics, ideally as efficient and accurate as possible. The majority of current techniques for network alignment rely on precomputed information, such as with protein sequence alignment, or on tunable network alignment parameters, which may introduce an increased computational overhead. Our presented algorithm, which we call Node Fingerprinting (NF), is appropriate for performing global pairwise network alignment without precomputation or tuning, can be fully parallelized, and is able to quickly compute an accurate alignment between two biological networks. It has performed as well as or better than existing algorithms on biological and simulated data, and with fewer computational resources. The algorithmic validation performed demonstrates the low computational resource requirements of NF.

History

Publication title

Journal of Computational Biology

Volume

21

Issue

10

Pagination

760-770

ISSN

1066-5277

Department/School

School of Natural Sciences

Publisher

Mary Ann Liebert Inc Publ

Place of publication

2 Madison Avenue, Larchmont, USA, Ny, 10538

Rights statement

Copyright 2014 Mary Ann Liebert, Inc.

Repository Status

  • Restricted

Socio-economic Objectives

Expanding knowledge in the biological sciences

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